MCP Servers

A directory of curated & open-source Model Context Protocol servers. Search and discover MCP servers to enhance your AI capabilities.

Weather API

An MCP server that enables AI assistants like Claude to retrieve and interpret real-time weather data.

Elevenlabs

Elevenlabs' Official MCP server that enables interaction with powerful Text to Speech and audio processing APIs.

BloodHound

An MCP server that enables LLMs to interact with and analyze Active Directory (AD) and Azure Active Directory (AAD) environments through natural language queries.

Just Prompt

A lightweight MCP server that provides a unified interface to various Large Language Model (LLM) providers.

Browser

A browser extension and MCP server that allows you to interact with the browser you are using.

Databutton

The easiest way to build custom tools for your AI assistant. No coding required.

Supabase

An MCP server that connects AI assistants directly with your Supabase project and allows them to perform tasks.

GitHub

GitHub's official MCP server that provides seamless integration with GitHub APIs.

ArXiv

An MCP server that provides a bridge between AI assistants and arXiv's research repository.

Google News

An MCP server implementation that provides Google News search capabilities via SerpAPI integration.

Backup

An MCP server that provides backup and restoration capabilities for AI agents and code editors like Cursor and Windsurf.

WireShark

An MCP server that empowers LLMs with real-time network traffic analysis capabilities using Wireshark's tshark.

Shopify

A Shopify API MCP Server that enables interaction with store data through GraphQL API.

Mobile Next

An MCP server that enables scalable mobile automation through a platform-agnostic interface.

G-Search

A powerful MCP server for Google search that enables parallel searching with multiple keywords simultaneously.

Fetch

An MCP server for fetching URLs / YouTube video transcripts.

ffmpeg

An MCP server to interact with ffmpeg for common media operations.

K8s

A Model Context Protocol (MCP) server that enables AI assistants like Claude to securely execute Kubernetes commands.

GPT Researcher

An MCP server that enables LLM applications to perform deep research via the MCP protocol.

Browserbase

An MCP server that allows LLMs to control a browser with Browserbase and Stagehand.

Pipedream

Run your own MCP server for over 2,500 apps and APIs, manage servers for your users, in your own app.

FAQs

Q: What exactly is the Model Context Protocol (MCP)?

A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.

Q: How is MCP different from OpenAI's function calling or plugins?

A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.

Q: Can I use MCP with frameworks like LangChain?

A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.

Q: Why was MCP created? What problem does it solve?

A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.

Q: Is MCP secure? What are the main risks?

A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.

Q: Who is behind MCP?

A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.

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